# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================
"""Contains the GaussianNoise layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import

from keras import backend
from keras.engine import base_layer
from keras.utils import tf_utils

import tensorflow.compat.v2 as tf

from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.layers.GaussianNoise')
class GaussianNoise(base_layer.BaseRandomLayer):
  """Apply additive zero-centered Gaussian noise.

  This is useful to mitigate overfitting
  (you could see it as a form of random data augmentation).
  Gaussian Noise (GS) is a natural choice as corruption process
  for real valued inputs.

  As it is a regularization layer, it is only active at training time.

  Args:
    stddev: Float, standard deviation of the noise distribution.
    seed: Integer, optional random seed to enable deterministic behavior.

  Call arguments:
    inputs: Input tensor (of any rank).
    training: Python boolean indicating whether the layer should behave in
      training mode (adding noise) or in inference mode (doing nothing).

  Input shape:
    Arbitrary. Use the keyword argument `input_shape`
    (tuple of integers, does not include the samples axis)
    when using this layer as the first layer in a model.

  Output shape:
    Same shape as input.
  """

  def __init__(self, stddev, seed=None, **kwargs):
    super(GaussianNoise, self).__init__(seed=seed, **kwargs)
    self.supports_masking = True
    self.stddev = stddev
    self.seed = seed

  def call(self, inputs, training=None):

    def noised():
      return inputs + self._random_generator.random_normal(
          shape=tf.shape(inputs),
          mean=0.,
          stddev=self.stddev,
          dtype=inputs.dtype)

    return backend.in_train_phase(noised, inputs, training=training)

  def get_config(self):
    config = {'stddev': self.stddev, 'seed': self.seed}
    base_config = super(GaussianNoise, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  @tf_utils.shape_type_conversion
  def compute_output_shape(self, input_shape):
    return input_shape
